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Non-invasive suppression of essential tremor via phase-locked disruption of its temporal coherence

brain Stimulation 2023年第1期16卷 276-277页

作者： Li, Junheng Schreglmann, Sebastian Wang, David Peach, Robert Laurate, Anna Rhodes, Edward Panella, Emanuele Cassara, Antonino Boyden, Edward Barahona, Mauricio Rothwell, John Bhatia, Kailash Grossman, Nir Department of Brain Sciences Imperial College London London UK UK Dementia Research Institute UK Institute of Neurology Department of Clinical and Movement Neuroscience Queen Square University College London (UCL) UK Computer Science and Artificial Intelligence Laboratory Massachussetts Institute of Technology (MIT) USA Department of Brain Sciences Imperial College London UK Department of Mathematics and EPSRC Centre for Mathematics of Precision Healthcare Imperial College London UK Department of Physics Imperial College London London SW7 2AZ UK IT’IS Foundation for Research on Information Technologies in Society Switzerland Department of Media Arts and Sciences MIT USA McGovern Institute for Brain Research MIT USA Howard Hughes Medical Institute Cambridge USA Department of Biological Engineering MIT USA Department of Brain and Cognitive Sciences MIT USA Centre for Neurobiological Engineering MIT USA Koch Institute for Integrative Cancer Research MIT USA Centre for Bio-Inspired Technology Department of Electrical and Electronic Engineering Imperial College London UK Centre for Neurotechnology Imperial College London UK

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AM-ConvBLS: Adaptive Manifold Convolutional Broad Learning System for Cross-Session and Cross-Subject Emotion Recognition

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IEEE Transactions on Affective Computing 2025年

作者： Lei, Chunyu Chen, C. L. Philip Zhang, Tong South China University of Technology Guangdong Provincial Key Laboratory of Computational Intelligence and Cyberspace Information The School of Computer Science and Engineering Guangzhou510006 China Pazhou Lab Brain and Affective Cognitive Research Center Guangzhou510335 China Ministry of Education on Health Intelligent Perception and Paralleled Digital-Human Engineering Research Center Guangzhou China

Emotion recognition based on electroencephalography (EEG) data has gained rapid development because of its believability and accuracy. However, most existing EEG emotion recognition methods suffer from three issues: 1) underutilization of multi-scale emotion representations, 2) underexploitation of emotion labels and intrinsic geometric structures, and 3) inherent non-stationarity characteristic and individual variability of EEG signals. To this end, we propose an Adaptive Manifold Convolutional Broad Learning System (AM-ConvBLS) to capture the multi-scale distribution aligned emotion patterns in the geometric structure preserved emotion submanifold space. To begin with, a Multi-Scale Representation Learning (MSRL) module is developed to learn diverse multi-scale emotion representations. The Domain Discrepancy Elimination (DDE) module is then developed to further align feature distributions in the source and target domains. To further utilize EEG emotion labels, we devise a Label Manifold Information Exploration (LMIE) module to retain the sample label consistency. In addition, a global feature importance analysis method for AM-ConvBLS based on Shapley Additive Global importancE (SAGE) is designed to investigate the EEG frequency band importance and brain neural activation patterns. Experiments on SEED, SEED-IV, and SEED-V datasets demonstrate the effectiveness and superiority of our AM-ConvBLS. © 2010-2012 IEEE.

关键词： Convolution

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Physion: Evaluating Physical Prediction from Vision in Humans and Machines

arXiv

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arXiv 2021年

作者： Bear, Daniel M. Wang, Elias Mrowca, Damian Binder, Felix Tung, Hsiao-Yu Fish Pramod, R.T. Holdaway, Cameron Tao, Sirui Smith, Kevin Sun, Fan-Yun Fei-Fei, Li Kanwisher, Nancy Tenenbaum, Joshua B. Yamins, Daniel L.K. Fan, Judith Department of Psychology StanfordCA94305 United States Department of Electrical Engineering StanfordCA94305 United States Department of Computer Science StanfordCA94305 United States Wu Tsai Neurosciences Institute StanfordCA94305 United States Department of Cognitive Science UC San Diego CA92093 United States Department of Psychology UC San Diego CA92093 United States Department of Brain and Cognitive Sciences and CBMM MIT CambridgeMA02139 United States

While current vision algorithms excel at many challenging tasks, it is unclear how well they understand the physical dynamics of real-world environments. Here we introduce Physion, a dataset and benchmark for rigorously evaluating the ability to predict how physical scenarios will evolve over time. Our dataset features realistic simulations of a wide range of physical phenomena, including rigid and soft-body collisions, stable multi-object configurations, rolling, sliding, and projectile motion, thus providing a more comprehensive challenge than previous benchmarks. We used Physion to benchmark a suite of models varying in their architecture, learning objective, input-output structure, and training data. In parallel, we obtained precise measurements of human prediction behavior on the same set of scenarios, allowing us to directly evaluate how well any model could approximate human behavior. We found that vision algorithms that learn object-centric representations generally outperform those that do not, yet still fall far short of human performance. On the other hand, graph neural networks with direct access to physical state information both perform substantially better and make predictions that are more similar to those made by humans. These results suggest that extracting physical representations of scenes is the main bottleneck to achieving human-level and human-like physical understanding in vision algorithms. We have publicly released all data and code to facilitate the use of Physion to benchmark additional models in a fully reproducible manner, enabling systematic evaluation of progress towards vision algorithms that understand physical environments as robustly as people do. Copyright © 2021, The Authors. All rights reserved.

关键词： Forecasting

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Noisy adaptation generates lévy flights in attractor neural networks 21

Noisy adaptation generates lévy flights in attractor neural...

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Proceedings of the 35th International Conference on Neural Information Processing Systems

作者： Xingsi Dong Tianhao Chu Tiejun Huang Zilong Ji Si Wu School of Psychology and Cognitive Sciences Peking Univerisity and IDG/McGovern Institute for Brain Research Peking University and PKU-Tsinghua Center for Life Sciences Academy for Advanced Interdisciplinary Studies Peking University School of Electronics Engineering and Computer Science Peking University School of Psychology and Cognitive Sciences Peking Univerisity and IDG/McGovern Institute for Brain Research Peking University PKU-Tsinghua Center for Life Sciences Academy for Advanced Interdisciplinary Studies Peking University

ISBN: (纸本)9781713845393

Lévy flights describe a special class of random walks whose step sizes satisfy a power-law tailed distribution. As being an efficient searching strategy in unknown environments, Lévy flights are widely observed in animal foraging behaviors. Recent studies further showed that human cognitive functions also exhibit the characteristics of Lévy flights. Despite being a general phenomenon, the neural mechanism at the circuit level for generating Lévy flights remains largely unclear. Here, we investigate how Lévy flights can be achieved in attractor neural networks. To elucidate the underlying mechanism clearly, we first study continuous attractor neural networks (CANNs), and find that noisy neural adaptation, exemplified by spike frequency adaptation (SFA) in this work, can generate Lévy flights representing transitions of the network state in the attractor space. Specifically, the strength of SFA defines a travelling wave boundary, below which the network state displays local Brownian motion, and above which the network state displays long-jump motion. Noises in neural adaptation cause the network state to intermittently switch between these two motion modes, manifesting the characteristics of Lévy flights. We further extend the study to a general attractor neural network, and demonstrate that our model can explain the Lévy-flight phenomenon observed during free memory retrieval of humans. We hope that this study will give us insight into understanding the neural mechanism for optimal information processing in the brain.

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Detection-and-Excitation Neural Network Achieves Accurate Nasopharyngeal Carcinoma Segmentation in Multi-modality MR Images

Detection-and-Excitation Neural Network Achieves Accurate Na...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Guihua Tao Haojiang Li Lizhi Liu Hongmin Cai School of Computer Science and Engineering South China University of Technology Guangzhou China Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy Collaborative Innovation Center for Cancer Medicine Sun Yat-sen University Cancer Center Guangzhou China Brain and Affective Cognitive Research Center Pazhou Lab Guangzhou China

ISBN: (纸本)9781665429825

Accurate and reliable segmentation of nasopharyngeal carcinoma (NPC) in magnetic resonance image (MRI) is important for treatment planning and follow-up evaluation. However, it is still challenging because NPC is infiltrative with a vague border and has a tiny volume with varying sizes and shapes. The above problems easily cause the NPC tumors’ features to submerge in the process of feature extraction. Standard segmentation methods do not cope with the “feature submergence” and performed unsatisfactorily in NPC segmentation. To address the problem, a dual-supervised method equipped with the detection-and-excitation module (DEM) was proposed. DEM strengthens the region of interest (ROI) and weakens complex and immense background through incorporating detection feature maps (outline maps) with intermediated segmentation features. To be specific, the DEM explored the outline of the ROI and output a detection feature map by using a supervised layer guided by a tailored loss function. Then, the DEM uses the results to recalibrates the intermediated segmentation. Finally, a neural network equipped with the proposed DEM was elaborately designed to achieve accurate segmentation. We applied the proposed network termed as DENet on a real nasopharyngeal carcinoma dataset to realize automatic NPC tumors segmentation in multi-modality MR images. The proposed DEM enables the neural network to segment NPC tumors within the immense and complex background. Experimental results have demonstrated the effectiveness of the proposed method by comparing it with benchmark models.

关键词： Image segmentation Shape Neural networks Semantics Feature extraction Planning Reliability

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Optimal approximate sampling from discrete probability distributions

arXiv

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arXiv 2020年

作者： Saad, Feras A. Freer, Cameron E. Rinard, Martin C. Mansinghka, Vikash K. Department of Electrical Engineering & Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Department of Brain & Cognitive Sciences Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electrical Engineering & Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Department of Brain & Cognitive Sciences Massachusetts Institute of Technology CambridgeMA02139 United States

This paper addresses a fundamental problem in random variate generation: given access to a random source that emits a stream of independent fair bits, what is the most accurate and entropy-efficient algorithm for sampling from a discrete probability distribution (p1, . . ., pn), where the probabilities of the output distribution (p^1, . . ., p^n) of the sampling algorithm must be specified using at most k bits of precision? We present a theoretical framework for formulating this problem and provide new techniques for finding sampling algorithms that are optimal both statistically (in the sense of sampling accuracy) and information-theoretically (in the sense of entropy consumption). We leverage these results to build a system that, for a broad family of measures of statistical accuracy, delivers a sampling algorithm whose expected entropy usage is minimal among those that induce the same distribution (i.e., is "entropy-optimal") and whose output distribution (p^1, . . ., p^n) is a closest approximation to the target distribution (p1, . . ., pn) among all entropy-optimal sampling algorithms that operate within the specified k-bit precision. This optimal approximate sampler is also a closer approximation than any (possibly entropy-suboptimal) sampler that consumes a bounded amount of entropy with the specified precision, a class which includes floating-point implementations of inversion sampling and related methods found in many software libraries. We evaluate the accuracy, entropy consumption, precision requirements, and wall-clock runtime of our optimal approximate sampling algorithms on a broad set of distributions, demonstrating the ways that they are superior to existing approximate samplers and establishing that they often consume significantly fewer resources than are needed by exact samplers. Copyright © 2020, The Authors. All rights reserved.

关键词： Entropy

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Generating 3D TOF-MRA volumes and segmentation labels using generative adversarial networks

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Medical Image Analysis 2022年 78卷 102396-102396页

作者： Subramaniam, Pooja Kossen, Tabea Ritter, Kerstin Hennemuth, Anja Hildebrand, Kristian Hilbert, Adam Sobesky, Jan Livne, Michelle Galinovic, Ivana Khalil, Ahmed A. Fiebach, Jochen B. Frey, Dietmar Madai, Vince I. CLAIM - Charité Lab for AI in Medicine Charité Universitätsmedizin Berlin Germany Department of Computer Engineering and Microelectronics Computer Vision & Remote Sensing Technical University Berlin Berlin Germany Berlin Germany Bernstein Center for Computational Neuroscience Berlin Germany Institute for Imaging Science and Computational Modelling in Cardiovascular Medicine Charité Universitätsmedizin Berlin Berlin Germany Fraunhofer MEVIS Max-von-Laue-Str. 2 Bremen Germany Department VI Computer Science and Media Beuth University of Applied Sciences Berlin Germany Johanna-Etienne-Hospital Neuss Germany Centre for Stroke Research Berlin Charité Universitätsmedizin Berlin Berlin Germany Department of Neurology Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany Mind Brain Body Institute Berlin School of Mind and Brain Humboldt University Berlin Berlin Germany Berlin Institute of Health Berlin Germany School of Computing and Digital Technology Faculty of Computing Engineering and the Built Environment Birmingham City University Birmingham United Kingdom QUEST-Center for Transforming Biomedical Research Berlin Institute of Health Charité Universitätsmedizin Berlin Charitéplatz 1 Berlin10117 Germany

Deep learning requires large labeled datasets that are difficult to gather in medical imaging due to data privacy issues and time-consuming manual labeling. Generative Adversarial Networks (GANs) can alleviate these challenges enabling synthesis of shareable data. While 2D GANs have been used to generate 2D images with their corresponding labels, they cannot capture the volumetric information of 3D medical imaging. 3D GANs are more suitable for this and have been used to generate 3D volumes but not their corresponding labels. One reason might be that synthesizing 3D volumes is challenging owing to computational limitations. In this work, we present 3D GANs for the generation of 3D medical image volumes with corresponding labels applying mixed precision to alleviate computational constraints. We generated 3D Time-of-Flight Magnetic Resonance Angiography (TOF-MRA) patches with their corresponding brain blood vessel segmentation labels. We used four variants of 3D Wasserstein GAN (WGAN) with: 1) gradient penalty (GP), 2) GP with spectral normalization (SN), 3) SN with mixed precision (SN-MP), and 4) SN-MP with double filters per layer (c-SN-MP). The generated patches were quantitatively evaluated using the Fréchet Inception Distance (FID) and Precision and Recall of Distributions (PRD). Further, 3D U-Nets were trained with patch-label pairs from different WGAN models and their performance was compared to the performance of a benchmark U-Net trained on real data. The segmentation performance of all U-Net models was assessed using Dice Similarity Coefficient (DSC) and balanced Average Hausdorff Distance (bAVD) for a) all vessels, and b) intracranial vessels only. Our results show that patches generated with WGAN models using mixed precision (SN-MP and c-SN-MP) yielded the lowest FID scores and the best PRD curves. Among the 3D U-Nets trained with synthetic patch-label pairs, c-SN-MP pairs achieved the highest DSC (0.841) and lowest bAVD (0.508) compared to the benchmark

关键词： Generative adversarial networks

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The Effects of Mismatch between SPECT and CT Images on Quantitative Activity Estimation – A Simulation Study

Research Square

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Research Square 2021年

作者： Lyu, Yingqing Chen, Yue Mok, Greta S.P. Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau Taipa China Department of Nuclear Medicine The Affiliated Hospital of Southwest Medical University Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province Sichuan Luzhou China Center for Cognitive and Brain Sciences Institute of Collaborative Innovation University of Macau Taipa China

Background: Quantitative activity estimation is essential in targeted radionuclide therapy dosimetry. Misregistration between SPECT and CT images at the same imaging time point due to patient movement degrades accuracy. This work aims to study the mismatch effects between CT and SPECT data on attenuation correction (AC), volume-of-interest (VOI) delineation and registration for activity estimation. Methods: Nine 4D XCAT phantoms were generated at 1, 24, and 144 hrs post In-111 Zevalin injection, varying in activity distributions, body and organ sizes. Realistic noisy SPECT projections were generated by an analytical projection and reconstructed with quantitative OS-EM method. CT images were shifted from -5 to 5 voxels as well as according to clinical reference corresponding to SPECT images at each time point. For AC effect, mismatched CT images were used for AC in SPECT reconstruction while VOIs were mapped out from matched CTs. For VOI effect, target organs were mapped out using mismatched CTs with matched CTs for AC. For registration effect, non-rigid registrations were performed on sequential mismatched CTs to align corresponding SPECT images, with no AC and VOI mismatch. Bi-exponential curve fitting was performed to obtain time-integrated activity (TIA). Organ activity errors (%OAE) and TIA errors (%TIAE) were calculated. Results: According to clinical reference, %OAE was larger for organs near ribs for AC effect, e.g., -2.58%±0.81% for liver. For VOI effect, %OAE was larger for small and low uptake organs, e.g., -11.94%±10.34% for spleen. %OAE was proportional to mismatch magnitude, e.g., 4.77%±1.41%, 12.01%±3.97% and 42.81%±6.38% for 1-, 2-, and 5-voxel mismatch for lungs. For registration effect, %TIAE were larger when mismatch existed in more numbers of SPECT/CT images, while no substantial difference was observed when using mismatched CT at different time points for registration reference. %TIAE was highest for VOI, followed by registration and AC, e.g., 37

关键词： Curve fitting

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Noncontact Doppler Radar-based Heart Rate Detection on the SVD and ANC

Noncontact Doppler Radar-based Heart Rate Detection on the S...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Qiuxia Shi Bin Hu Fuze Tian Qinglin Zhao Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou University School of Computer and Communication Lanzhou University of Technology Ministry of Education Key Laboratory of Special Functional Materials and Structural Design (Lanzhou University) Lanzhou China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University & Institute of Semiconductors Chinese Academy of Sciences Engineering Research Center of Open Source Software and Real-Time System(Lanzhou University) Ministry of Education Key Laboratory of Special Functional Materials and Structural Design Ministry of Education Lanzhou University Lanzhou China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

In the Doppler biological radar-based applications of noncontact measurement of vital signs, effectively extracting heartbeat information from weak thoracic mechanical motion is an important problem to be solved. This study is aimed at extracting heartbeat signal via the technology combined with Short Time Fourier Transform (STFT), Singular Value Decomposition (SVD) and Adaptive Noise Canceller (ANC) from radar recording. The simulated data and the data collected by Doppler radar biosensor realized in laboratory are employed to validate the proposed method. The results show that the proposed method has the ability of detection for the heart rate and heart rate variability indexes in rest state, it has certain advantages in time-consuming and detection accuracy. Therefore, the current method provides another way to process vital sign signals recorded by Doppler radar.

关键词： Biomedical measurement Noise cancellation Time measurement Doppler radar Recording Doppler effect Biosensors

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Emotion Recognition under Sleep Deprivation Using a Multimodal Residual LSTM Network

Emotion Recognition under Sleep Deprivation Using a Multimod...

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International Joint Conference on Neural Networks (IJCNN)

作者： Le-Yan Tao Bao-Liang Lu Department of Computer Science and Engineering Center for Brain-like Computing and Machine Intelligence Shanghai China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai China Brain Science and Technology Research Center Shanghai China Qing Yuan Research Institute Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

Emotion recognition under sleep deprivation is instructive for the study of mental disorders such as major depressive disorder. Previous studies on emotion recognition under sleep deprivation have been mainly based on psychological research techniques. In this paper, we introduce a multilayer weight-sharing multimodal residual LSTM network for emotion recognition under sleep deprivation. The advantage of our proposed method is that it allows for the combination of three different features: the electroencephalography (EEG) single-channel differential entropy (DE) features, EEG functional strength features with topological correlation connectivity, and eye movement features. The experiments under the conditions of sleep deprivation, sleep recovery and baseline are designed and conducted. The experimental results demonstrate that the proposed method significantly enhances the performance compared with the simple concatenation of the features of different modalities, and the best mean accuracies of 86.86% and 82.03% are achieved for four emotions (happiness, sadness, fear, and neutral) in subject-dependent and cross-subject emotion recognition tasks under 30 hours of sleep deprivation, respectively. The classification accuracy of the happiness emotion is obviously impaired under sleep deprivation, indicating that sleep deprivation impairs the stimulation of the happiness emotion, and one night of sleep recovery can reactivate the elicitation of the happiness emotion to the baseline level. Furthermore, we study the brain neural patterns of the four emotional states. The prefrontal area becomes less activated for the happiness emotion and sadness emotion in the gamma band under sleep deprivation, while the neural pattern of the fear emotion is highly robust with respect to sleep deprivation.

关键词： Feature extraction Electroencephalography Sleep Emotion recognition Correlation Physiology Task analysis

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